Vibrational spectroscopy for quantitative measurement of analytes

ABSTRACT

The present invention relates to systems and methods for the determination of the secondary structure composition of proteins using coherent two-dimensional infrared (2DIR) spectroscopy of backbone amide I vibrations (1580-1720 cm −1 ). Fractions of α-helix, β-sheet, and unassigned regions in globular proteins were determined by singular value decomposition using basis spectra from sixteen commercially-available proteins with known crystal structures. Preferred methods included removing each protein from the set and comparing the predicted composition against the crystal structure. The root-mean-squared (RMS) errors of the predicted secondary structure compositions were found to be 7.9% for α-helix, 5.5% for β-sheet, and 7.6% for unassigned regions. The structure analysis can also be performed using one-dimensional absorption spectra and the RMS errors are compared with those obtained from 2DIR.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under Grant No.CHE-0911107 awarded by the National Science Foundation. The governmenthas certain rights in this invention.

BACKGROUND OF THE INVENTION

Modern structural biology seeks to establish a molecular understandingof biological function by exploring structure-function relationships inbiomolecules. The mechanisms for protein folding, protein-proteininteractions, ligand binding and catalysis are fundamentally linked tostructure. Membrane proteins, in particular, drive key biologicalprocesses such as cell-signaling, and ion transport through the cellmembrane. Despite the abundance, diversity, and biological importance ofmembrane proteins, relatively few crystal structures have been solveddue to the difficulty in crystallizing the proteins. Modern tools suchas cryo-electron-microscopy or x-ray crystallography enable detailedmeasurements of protein structure on multiple length-scales; however,structures are usually measured under non-biological conditions wherekey dynamic effects that lead to protein stability and function are notcaptured. Aside from NMR spectroscopy, most bio-analytical tools in usetoday are not sensitive to protein structure, heterogeneity, orconformational dynamics. Specific classes of proteins such as fibrousproteins, intrinsically disordered proteins, gels, amyloids, andaggregates, have been particularly difficult to characterize due to alack of structural techniques available.

Infrared spectroscopy in the amide I region has been used to measureprotein structure in solution. The vibrational modes in these regionsinvolve primarily backbone C═O and N—H vibrations that are relativelysensitive to secondary structure and are largely free from the influenceof side-chain absorptions. To date, however, most infrared measurementshave offered either qualitative information or have required significantefforts to vibrationally isolate individual residues through isotopelabeling. Attempts to extract structural information from amide-I and IIabsorption bands in proteins have relied on complex deconvolution andfitting to analyze largely featureless absorption bands. The results ofthese measurements are heavily dependent on the specific set offrequencies and fitting functions used, which greatly limits theusefulness of linear infrared spectroscopy as a structural tool.

SUMMARY OF THE INVENTION

The present invention relates to vibrational spectroscopy as a techniqueto measure structures of analytes. The systems and methods utilize thefact that secondary structures have specific signatures which allow fora quantitative decomposition of an analyte spectrum into combinations ofdifferent secondary-structure spectra. Preferred embodiments utilizemultidimensional infrared spectral measurements to measure and analyzeconformational structures at the molecular level. The present inventionprovides quantitative characteristics of analytes and dynamic propertiesof these analytes undergoing conformational change.

Various implementations of 2DIR have been devised in which excitationand detection frequencies have been measured either in the time orfrequency domain. One method involves using a pair of two IR pulses toexcite a sample and a third pulse to stimulate emission. The emittedsignal is often detected in the frequency domain using a gratingspectrometer, although time-domain detection has also been demonstrated.The two excitation pulses are delayed in time and the signal spectrum isoften measured as a function of the time delay between these two pulses.A numerical Fourier-transform along the excitation time delay yields theexcitation frequency. In order to produce a two-dimensional spectrum,the amplitude and phase of the emitted electric field must be measured.This is achieved either by using an external reference beam, or by usingthe third (detection) pulse as an internal reference. If the twoexcitation pulses are collinear, then the signal is emitted in the samedirection as the detection pulse. However, if the excitation pulses arenon-collinear, the signal is emitted in a background free direction.

In preferred embodiments of the present invention, the two excitationpulses are focused non-collinearly at the sample, and the emitted signalis overlapped with an external reference pulse at the detector.Different methods can be used to store, display and process data inaccordance with the invention. The two (or more) axes used for spectralanalysis can include excitation frequency versus detection frequency,time versus frequency, time versus time, etc. The pulses can be shapedto further modulate the conditions of a measurement.

Preferred embodiments of the invention provide for the quantitativemeasurement of different states of an analyte such as different isomericstructures or the different conformations of proteins or the rate ofchange of conformational structure undergoing dynamic changes, i.e. as afunction of time.

The present invention provides a simple and reliable analysis toquantify the percentage of amino-acids in α-helix or β-sheetconformations of proteins in solution. Multidimensional spectroscopy hasbeen developed as a technique to characterize complex systems insolution. In particular, coherent two-dimensional infrared (2DIR)spectroscopy has the ability to directly measure the structure, staticdisorder, conformational flexibility, and solvent exposure of residueswithin a protein. 2DIR spectroscopy can provide new insights intoprotein structure and heterogeneity, protein dynamics, protein-proteininteractions and ligand binding. However, the amount of structuralinformation extracted from 2DIR has remained largely qualitative, and inmost cases the equilibrium structure of the protein under investigationis known a priori. The present invention utilizes thestructural-sensitivity of 2DIR spectroscopy in order to develop aquantitative method to measure secondary structures of analytes.

Amide-I vibrations involve primarily combinations of backbone carbonylstretches. The ‘local’ carbonyl stretches, corresponding to eachindividual amino acid linkage within the protein, form the basis for thedelocalized normal modes. The frequencies of the normal modes are givenby the frequencies of the local C═O stretches as well as the magnitudeof the couplings between these local oscillators. Coupling patternsreport on the secondary structure and conformational disorder of theresidues: structured regions exhibit highly regular coupling patternswhereas disordered regions exhibit a random coupling pattern. Eachoscillator within a particular secondary structure is coupled stronglyto the other oscillators in the secondary structure giving risedelocalized normal modes. The frequency of the delocalized vibrationstends to separate into particular ranges: β-sheets exhibit two peaksnear 1630 and 1680 cm⁻¹ whereas α-helices and unstructured regionsappear near 1650 cm⁻¹. Despite the secondary structure-sensitivity ofthe peak frequencies, conformational disorder and solvent exposurerender the absorption bands broad and largely featureless.

Analogous to NMR spectroscopy, a 2DIR spectrum can be interpreted asfollows: the vibrations of the samples are first excited, or labeled, bya set of two infrared pulses, and after a certain waiting time, a thirdpulse stimulates the emission of the modes within the sample. Theexcitation frequency (horizontal axes) and a detection frequency(vertical) are then combined in a two-dimensional plot. Diagonal peaksare due to excitation and detection at the same frequencies, and crosspeaks arise from excitation of one vibration, and detection of adifferent vibration. Cross peaks are observed only if two vibrationsshare common atoms, or in this context, common residues. 2DIRspectroscopy offers enhanced structure-sensitivity by spreading thespectral content onto two frequency axes. Cross peaks are usuallyobserved if two vibrational modes are coupled (i.e. if the twovibrations share common residues). For example, in the case of β-sheets,off-diagonal features are observed between the two main peaks at 1630and 1680 cm⁻ with a corresponding cross peak, however negligible crosspeaks are observed between α-helix and β-sheet vibrations since theseinvolve different residues. Further analysis of the main spectralfeatures associated with each secondary structure is provided in thediscussion section below.

Systems in accordance with preferred embodiments of the inventioninclude a spectral data measurement system and a data processing systemthat is programmed to analyze spectral data for the quantitativemeasurement and analysis of analytes. An electronic display can displaythe analyzed data which can also be stored in electronic memory and/ortransmitted via private or public computer networks. The system uses alight source system, an optical system for generating light pulses thatilluminate the analyte to be measured and a detector system that detectslight from the analyte in response to the illuminating light. Motarizedstages can be used to control positioning of optical elements within theoptical system for light pulse control and shaping.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a system for multidimensional IR spectralmeasurements in accordance with preferred embodiments of the inventionincluding detector, controller, and flowchart of the frequency division;the rectangular boxes with sharp corners represent lasers and those withrounded corners represent electronic devices including a computer and adetector. The flow of the electronic triggers is represented by thedashed arrows connecting the electronic component: BS (orange rectanglebar), beam splitter; C (green rectangle bar), compensator; LO, localoscillator; T, tracer; P (−), polarizer; WP (=), half-wave plate; FM,flipper mirror; PM, parabolic mirror; S, sample; A, analyzer; F,low-pass filter, L1, CaF₂ lens (f=11 cm); L2, BK7 lens (f=10 cm); MO,monochromator;

FIG. 1B illustrates another preferred embodiment of a spectrometer inaccordance with the invention;

FIG. 2 PDB Structures of the proteins used for SVD analysis. α-helix andβ-sheet structures are colored in red and blue respectively; Theproteins are arranged in order of increasing β-sheet contents ascalculated by the DSSP program;

FIG. 3 illustrates amide-I linear absorption spectra in which peaks arenormalized to the area in the 1580 to 1720 cm⁻¹ and vertically offsetfor clarity;

FIG. 4 Correlation 2DIR spectra of the proteins acquired in theperpendicular polarization geometry. Contours are plotted from +/−50% ofthe maximum amplitude in 4% intervals and spectra are arranged in orderof increasing β-sheet content (see FIG. 2);

FIG. 5 includes SVD basis spectra wherein contours are plotted from+/−50% of the maximum amplitude in 4% intervals and spectra are arrangedin order of increasing β-sheet content (see FIG. 2);

FIG. 6 Comparison between amount of secondary structure predicted by SVDanalysis of the 2DIR spectra and the amount extracted from the x-raystructures using the DSSP program;

FIG. 7 illustrates a process sequence for obtaining and analyzingspectral data in accordance with preferred embodiments of the invention;

FIG. 8 is a 2D spectrum illustrating the correlation coefficient toindicate connections between conformations.

FIG. 9A illustrates a more detailed analysis of cross peaks of DNAspectral data in accordance with preferred embodiments of the inventionin which specific spectral features are labeled for an unfolded TBAaptamer;

FIG. 9B graphically illustrates spectral cross peaks to identify andcharacterize ubiquitan hydrogen/deuterium (H/D) exchange in a sample;

FIGS. 10A-10E illustrate the use of isotope labeling to distinguishamong folded, frayed, disorder, extended disorder and bugled turnconformations;

FIGS. 11A-11C illustrate method for analyzing DNA or RNA structuresusing aptamers which preferentially bind to target molecules;

FIG. 12 summarizes methods for resolving different isomers includingstructural, conformational, geometric and optical features;

FIG. 13 illustrates a 2D infrared analysis of blood analytes;

FIG. 14 illustrates the resolution of components in isomeric mixtures,such as nitrophenols;

FIGS. 15A and 15B illustrate the use of cross peak analysis to separatemixtures.

DETAILED DESCRIPTION OF THE INVENTION

The two-dimensional spectrometer system 10 is illustrated in FIG. 1A.The system provides for the acquisition of 2D IR spectra and relateddispersed vibrational echo (DVE) spectra, the synchronization offemtosecond 15 and T-jump laser 50 systems, and the acquisition oftransient 2D IR and DVE spectra. The optical components used to generateand detect the nonlinear signal is shown in FIG. 1A. A Ti:sapphireoscillator system 14, 16 (Tsunami, Spectra-Physics) is used to obtain aninitial femtosecond pulse, which is amplified by a regenerativeamplifier 18 (Spitfire, Spectra-Physics). The amplified pulse pumps anoptical parametric amplifier 20 (OPA). The signal and idler fields arefocused onto the AgGaS₂ crystal (C) to generate a 90 fs [full width athalf maximum] (FWHM) mid-IR pulse centered at 6 um (FWHM 160 cm⁻¹) bydifference frequency mixing. A single IR pulse is divided into fouridentical pulses by 4-mm-thick 50:50 ZnSe beam splitters (BS)(BS1516Z50050S, Rocky Mountain Instrument). ZnSe compensators(W11012Z550BA2+, Rocky Mountain Instrument) are used to maintain thesame chirp character between the transmitted and reflected pulses. Eachfemtosecond pulse travels to and is reflected back by a retroreflector48 (3 in. in diameter, PLX). The positions of the retroreflectors forbeams a and b, and LO (tracer) are controlled by the motorized linearstages adjusted by stage controller 106 (ANT-50L, Aerotech) to adjustthe relative delay between pulses with a resolution of 10 nm (0.067 fs),an accuracy of 300 nm (2 fs), and repeatability of 50 nm (0.33 fs). Beamc is chopped at 500 Hz. The fourth beam is further split into the localoscillator (LO) and the tracer (T). The tracer beam is only sent to thesample by the flipper mirror (FM) when the pump-probe signal iscollected. The polarization of each beam is controlled by a wire-gridpolarizer (P) (IGP229-25HER-0921, Molectron) and MgF₂ half-wave plate(WP) (MWPMFA2-22-6M, Karl Lambrecht Corp.) pair. The energy in each ofthe identical pulses a, b, and c after the polarizer and half-wave platepair is 0.15 μJ, for example. All beams are focused onto the sample (S)by a gold-coated off-axis (90% angle) parabolic mirror (PM) (3 in. indiameter, f=10 cm, A8037-308, Janos Technology) within the spot size of100-110 μm in diameter at 90% transmission. The samples were placed in atemperature-controlled brass cell consisting of two 1-mm-thick and1-in.-diam CaF₂ windows separated by a 50 μm thick Teflon spacer. Thetemperature of the cell can be regulated to ±0.1° C. by a circulatingwater bath. The quoted temperatures are those measured on the CaF₂window by a thermocouple.

A preferred embodiment of the invention relates to the measurement ofconformational structure of proteins. Transient 2D IR spectrometerprovide for direct probing of protein unfolding over time scales from −2ns to 50 ms following a Tjump. Synchronization of the femtosecond IRlaser system and T-jump laser with minimal timing error relies ondivision of the stable reference clock signal (82 MHz) from theTi-:sapphire oscillator down to 1 kHz and 20 Hz. Control of the timedelay is provided to within 2 ns accuracy. The reduced repetition rateof the T-jump laser implies a longer data collection time. To overcomethis difficulty, 2D IR spectra are obtained by undersampling the dataacquisition points by a factor of 3.5. For an improved signal-to-noiseratio, also perform a balanced heterodyne detection of the nonlinearsignal with a dual stripe array detector. The T-jump induced phasejitter in heterodyne detection is suppressed by propagating the localoscillator field through the T-jump region of the sample.

The three beams (a, b, and c) and the tracer propagate parallel to eachother centered at the four corners of the 1 inch square box before beingfocused. The nonlinear signal field is generated in thewave-vector-matched direction k_(s)=k_(a)+k_(b)+k_(c), ideally the samedirection as the tracer. The LO enters the sample between the tracer andbeam c. A time delay of 35 ps between the LO and three beams at thefocal point on the sample ensures that the LO is separated from thethree pulses by much longer than the amide I vibrational lifetime, butis much shorter than the T-jump pulse width. It therefore does notgenerate nonlinear signals. However, the LO experiences the same densityperturbations in the sample as the other pulses as the probe beam doesas pump beam in pump-probe spectroscopy. (Therefore, T-jump 2D IRspectroscopy will be directly applicable to another kind of 2D IRspectroscopy based on pump-probe spectroscopy.) The LO is overlappedtemporally with the third order signal at the array detector 250.

After passing through the sample cell, the LO and the third order signalare allowed to propagate where beams a, b, and c are masked. The LO iscombined with the signal on either side of a 50:50 beam splitter afterbeing picked off by M2. The reflected third order signal and transmittedLO pair (solid lines) are focused onto the upper stripe of the dualstripe (2×64) array mercury cadmium telluride (HgCdTe) (MCT) detector250 (IR-0144, Infrared Systems Development) after being dispersed by a190 mm monochromator (Triax 190, Jobin Yvon) with a 40 lines/mm grating.The other pair (dashed lines) propagates over the mirror (M3) and isfocused onto the lower stripe. The analyzer (A) determines thepolarization of the third order signal and the low-pass filter (F)removes the scattered T-jump pulse. A CaF₂ lens (L1, f=11 cm) focusesthe third order signal and LO pairs onto the slit (0.2 mm) of themonochromator 200.

A data acquisition circuit 108 can be used for initial data processingbefore the spectral data is transmitted to data processor or computerfor processing and display on the electronic display 102 and storage inmemory 104. The computer can also be used to control system operations,such as motarized stage controller 106 and light sources. Delaygenerator circuits 22, 46, frequency dividers 24, 44 and chappercontroller 42 are used to regulate pulse timing.

2D IR data were taken either in the parallel (ZZZZ) or in the crossed(ZZYY) polarization geometry. Rephasing (k_(s)=k₁+k₂+k₃≡k_(R)) andnonrephasing (k_(s)=k₁−k₂+k₃≡k_(m)) spectra are NR, obtained for τ₂=100fs by altering the time sequence of the k_(a) and k_(b) pulses.Dispersed heterodyned signals are collected onto the array detector witha spectral resolution of −4 cm⁻¹ in the ω₃ dimension. To reduce dataacquisition time, undersample the data as a function of τ₁ in 14 fssteps from 0 to 2.1 ps for the rephasing and from 0 to 1.2 ps for thenonrephasing configurations, respectively. A Fourier transform of the τ₁axis yields the individual 2D rephasing and nonrephasing spectra, andthe sum of these gives the absorptive 2D IR correlation spectrum. Theactual frequency ω₁ for the data collected by undersampling is obtainedby reflecting the transformed frequency (ω_(1u)) to the Nyquistfrequency (ω_(N)) asω₁−2ω_(N)−ω_(1u)  (1)The resolution in the ω₁ dimension after Fourier transformation is 0.5cm⁻¹. FIG. 1B illustrates another preferred embodiment utilizing threeIR beams to generate a third order signal in the sample. The LO beamdelay is stopped using the piezo controlled stage.

The system 400 that illuminates the sample (S) 402 and detects 404 thetransmitted signal is enlarged and shows the beam geometry in thesectional view 420. The mirrors PM are symmetric about the sample sothat noncolinear beams converge on the sample and subsequently separatedfor detection.

Additional details regarding systems and methods used in connection withpreferred embodiments of the invention are described in InternationalApplication No. PCT/US2008/010460 filed Sep. 8, 2008, claiming priorityto U.S. Application No. 60/967,889 filed on Sep. 7, 2007 and furthercorresponding to U.S. application Ser. No. 12/676,536 filed Mar. 4,2010, the entire contents of the above application being incorporatedherein by reference.

The set of proteins used in connection with the present invention wereFIG. 2 selected to span a wide variety of structures ranging from mainlyα-helical proteins (Myoglobin) to mainly β-sheet proteins (γ-globulins).The solubility, robustness, and commercial availability of each proteinwere also considered. All samples were obtained from Sigma-Aldrich andwere used without further purification. The samples werehydrogen/deuterium exchanged in D₂O (Cambridge Isotopes, Andover Mass.)at 60° C. for 1 hour, and lyophilized at −210° C. Solid protein sampleswere stored at −20° C. to prevent degradation. The proteins were thenredissolved in pure D₂O at neutral pH* to a final concentration of 20mg/ml. Fibrigonen, Immunoglobulin G and Insulin samples were dissolvedin pH*=2 DCl solution. The solutions were placed in atemperature-controlled sample cell equipped with two CaF₂ windows and a50 μm Teflon spacer. Experiments were carried out at 25° C.

The x-ray structure of each protein was obtained from the BrookhavenProtein Databank (PDB) and the secondary structure assignment wascalculated with the DSSP program. DSSP uses the atomic coordinates andhydrogen-bonding patterns to assign each residue to the followingstructural elements: β-bridge, extended β-strand, 3-10 helix, α-helix,hydrogen bonded turns, and bends. For SVD analysis we combined theβ-bridge and extended β-strand into a single structural element β-sheet,and combined the α-helix and 3-10 helix residues into an effectiveα-helix, all other residues were labeled unassigned.

Singular value decomposition (SVD) is a matrix-diagonalization techniqueused to extract the principal spectral component spectra that describethe dataset. Within this method a matrix of spectra M, is decomposedinto three matrices such that,M=USV ^(T),  (2)where the superscript T indicates a transpose of V. The SVD process isanalogous to eigenvalue decomposition: Column-wise the matrix U containsa set of orthogonal basis spectra or SVD components, row-wise V containsthe set of coefficients associated with the projection of each inputspectrum (M) onto the individual basis spectra (U) and S is a diagonalmatrix containing a set of weighting coefficients indicating therelative contribution of each basis vector in U to the input data set M.For preferred embodiments, FTIR spectra are input as one-dimensionalarrays (vectors) where each entry corresponds to the absorbance atdifferent frequencies, for example in the amide I region from 1580 to1720 cm⁻¹. Two-dimensional spectra (in this sample, in the 1580 to 1720cm⁻¹ cm region) are likewise column-wise reshaped into one dimensionalarrays. In the 2DIR example the size of the matrices are as follows:M=7396×15, U=15×7396, S=V=15×15

Some of the U columns, V columns, and S diagonal values are zero or nearzero. We discard these components and only keep the nonzero parts. As aresult of this rejection, S becomes a square, invertible matrix.

If we assume that the protein spectra, M, are the linear combination ofthe secondary structure spectra weighted by their content, then we candecompose the spectra into:ΣP ¹ =M  (3)where Σ={m^((β)), m^((α)), m^((una))} and P={p^((β)), p^((α)),p^((una))}. The columns of Σ are the pure spectra of a β-sheet, α-helix,and unassigned structure, respectively. The columns of P contain thefraction of residues per protein in the β-sheet, α-helix, and unassignedconformation calculated using the DSSP program.

To transform the SVD results from equation (2) into the spectra andfractions in equation (3), we insert XX⁻¹=I into equation (1):M=USXX ⁻¹ V ^(T).  (4)and solve for X⁻¹ such that:X ⁻¹ V ^(T) =P ^(T).  (5)can then be inverted and multiplied to give Σ:USX=Σ.  (6)

To assign the secondary structure P^((u))={p^((β))(u), p^((α))(u),p^((una))(u)} of an “unknown” spectrum m^((u)), we solve the equation:Σ(P ^((u)))^(T) =m ^((u)).  (7)Substitution of previous equations allows us to solve for P^((u)) with:P ^((u)) =P ^(T) VS ⁻¹ U ^(T) m ^((u)).  (8)

The SVD procedure is cross-validated for each sample by removing aspecific spectrum from the initial set, building an SVD basis with theremaining spectra, and using the removed spectrum as the unknown.Finally the predicted percentage of secondary structure is compared withthe percentage computed from the PDB structure.

Measured IR absorption and 2DIR spectra are shown in FIGS. 3 and 4respectively. Qualitatively, absorption spectra associated withprimarily α-helical proteins are characterized by a single band centerednear 1650 cm⁻¹ with an approximate diagonal width of 50 cm⁻¹, whileproteins composed of primarily β-sheet show two peaks centered near 1620and 1680 cm⁻¹ resulting from vibrations whose main transition dipoleslie perpendicular and parallel to the β-strands respectively. Theamplitude ratio of ν_(parallel) to ν_(perp) reports on the size of theβ-sheet. Described herein is the analysis to the total contents ofα-helix and β-sheet which can be applied to other structures. 2DIRspectroscopy is sensitive to the size and shape of secondary structures;however a larger and more diverse basis set would be required for suchanalysis. Mixed α/β proteins show a combination of the featuresassociated with helix, sheet and unstructured regions where the relativecontributions of the individual features depends on the fraction ofresidues that compose each secondary structure. Peaks assigned tounstructured regions are centered near 1640 cm⁻¹ and thus tend tooverlap with the α-helix peaks. This overlap increases the difficulty ofunambiguously assigning α-helix and unstructured conformations inproteins. In general, diagonal linewidths report on the staticheterogeneity (disorder) of the amide-I frequencies whereas theanti-diagonal linewidths report on the sub-picosecond dynamicfluctuations that are a result of protein-solvent interactions; and theratio of diagonal to anti-diagonal linewidth serves as a measure ofstructural rigidity and solvent exposure. In this context it should bepointed out that since vibrations are highly delocalized residue-levelresolution is often obtained through isotope labeling.

Once spectra are decomposed by SVD analysis and structure vectors arecomputed (eq. 3), it is informative to reconstruct the 2DIR spectracorresponding to purely α-helix, β-sheet, and unassigned components (eq.6). FIG. 5 below shows the reconstructed SVD spectra; the α-helixspectrum shows a single round peak centered at 1650 cm⁻¹ whereas theβ-sheet spectrum exhibits two peaks near 1630 and 1670 cm⁻¹ with thecorresponding cross-peaks, giving the spectrum a characteristic“Z-shape” associated with primarily β-sheet proteins. SVD spectra are inagreement with our spectral assignment described in the previous sectionas well as with experimental and simulated spectra of idealizedstructures published previously. The component spectrum associated withthe unassigned structures features a peak centered near 1640 cm⁻¹ with adistinct diagonal elongation due to the disorder associated withunstructured regions. Compared to spectra of unstructured proteins, theunassigned SVD spectrum shows a somewhat more pronounced diagonalelongation. In addition, a horizontal ridge near ω_(deletion)=1680 cm⁻¹characteristic of β-sheet spectra is observed, indicating that theunassigned peak contains a small amount of β-sheet character, and maysuggest that crystal structures underestimate the amount of β-sheetpresent in solution. This effect can contribute to the uncertainty inpredicting secondary structure compositions by SVD analysis.

The SVD method is validated by removing each protein from the initialset, creating an SVD basis with the remaining structures, and using thenew basis to analyze the “unknown” structure. FIG. 6 shows the fractionof each secondary structure predicted from SVD analysis along with thefractions obtained from the x-ray structure. Overall there is anexcellent correlation between the predicted structures in solution withthe compositions derived from the crystal structure. Not surprisinglythe largest error is observed in Myoglobin where the protein liesoutside of the conformational space spanned by the SVD basis set.Myoglobin was therefore removed from the root-mean-squared errorcalculation in this example. The expected result would be to see a largeerror for γ-globulins since this structure lies at the opposite extremeof the conformational space sampled by the SVD basis set, however theerror is small, due to the fact that other proteins in the set such asImmunoglobulin G have a similar structure. The root-mean-squared errorsin comparing the SVD prediction with the x-ray structure are 7.9, 5.5,and 7.6% for α-helix, β-sheet, and unstructured components respectively.Naturally, the β-sheet component is the most distinct and thus theerrors in predicting the β-sheet structure are smaller.

Distinguishing between α-helices and unstructured regions has remainedchallenging for amide-I infrared spectroscopy as both peaks appear inthe same region of the spectrum. As shown in FIG. 5, the two structurescan be distinguished by the ratio of diagonal to anti-diagonallinewidths, making 2DIR a more sensitive probe of structure. Forcomparison, SVD decomposition using the FTIR spectra was performed. TheRMS errors obtained for an FTIR-derived SVD basis are: 19.5, 8.3 and21.5% respectively. This comparison highlights thestructural-sensitivity gain associated with projecting the spectralinformation onto two frequency axes and measuring the diagonal as wellas the anti-diagonal linewidths. It is important to point out that,unlike FTIR spectroscopy, 2DIR spectra not affected by small changes ofthe H₂O background as the non-linear interactions largely suppress thebroad background signal. Incomplete background subtraction, one of thelargest sources of error in FTIR spectroscopy, has negligible effects onthe 2DIR spectra.

Circular dichroism (CD) spectroscopy is a method to determineconformation and conformational changes of proteins in solution.Although the light-matter interactions of amide-I IR spectroscopy andultraviolet dichroism spectroscopy are quite different, both methodsmeasure the structure of the backbone of proteins by excitingtransitions that are delocalized over the amide-I units and where thelocal structure of the units affects the absorption frequencies andintensities. The errors associated with secondary structuredetermination by 2DIR are comparable to those obtained from circulardichroism where the spectra are assumed to be a linear combination ofthe individual structure-spectra. These RMSE values are: 9% α-helix, 12%anti-parallel β-sheet, 8% parallel β-sheet, 7% β-turn, and 9%unassigned. These results show that 2DIR spectroscopy is a viablealternative to CD spectroscopy with the added advantage of ultrafasttime resolution and the ability to isotope-label individual residues forincreased structural resolution.

The observed (˜8%) errors between structure determination techniques insolution compared to crystal structure likely reflect, in part, on thestructural heterogeneity and the inherent differences in structure thatare induced upon crystallization. Water can play a central role inmaintaining the balance between entropic and enthalpic contributionsthat determine the structure and conformational flexibility needed forprotein function.

Another source of error in measurement stems from the SVD basis itself.The SVD basis must reflect the region of conformational space of theunknown protein to be analyzed. For example, constructing a basis set ofpurely β-sheet proteins and using such basis to analyze an α-helicalprotein would likely result in a large error. A particularly large basisset is unlikely to capture the structural details of an individualproteins and therefore likely to produce an averaged-out measure ofstructure. To circumvent this limitation, it may be possible to performa multi-level SVD analysis where the protein is initially screened usinga large basis set, and based on the initial results the SVD basis isthen further refined to include only proteins that are similar instructure to the unknown protein. However such analysis would besusceptible to errors in biasing the basis set towards a particularstructure that may not accurately reflect the structure of the unknownprotein.

Computational models and symmetry considerations for idealized β-sheetssuggest that 2DIR spectroscopy is sensitive to the size and type ofβ-structure. To date a systematic measurement of pure β-sheet proteinshas not been carried out. More generally, quantifying the spectralsignatures of various structural motifs, with particular attention toheterogeneity and site disorder, will further enhance the capabilitiesof 2DIR spectroscopy as a structural technique.

Thus, preferred embodiments provide systems and methods to measureprotein structure in solution based on ultrafast two-dimensionalinfrared spectroscopy. Spectra are analyzed by singular-valuedecomposition, a analysis technique that is not affected by thesubjectivity of phenomenological fitting models. The model was tested ona set of sixteen commercially-available globular proteins withwell-defined structures. The root-mean-squared errors compared to thecrystal structures are 7.9, 5.5, and 7.6% for α-helix, β-sheet, andunstructured components respectively. The results show that 2DIRspectroscopy is capable of quantitative structure determination on stockproteins without the need for labeling. The key advantage, however, of2DIR spectroscopy lies in its picosecond time resolution and ability tospread spectral information over two axes by correlating pump and probeenergies. Combined with a laser-induced temperature-jump the method canbe used to determine protein folding/unfolding pathways in solution andexpand the current understanding of the structural-changes associatedwith protein folding and function.

Quantify secondary structures at additional features or characteristicssuch as the type of local hydrogen bond registry or nanometer scalestructures, break down beta-sheets into parallel, antiparallel,beta-barrel, cross-beta-strand structures and/or identify alpha helicesusing right handed vs. left-handed configuration or α-helix vs 3/10helix. Additional characteristics include beta-hairpin andbeta-alpha-beta.

Quantitative information such as the size of structures (how manystrands in a sheet, how long is each helix) can also be measured.

Further preferred embodiments provide for characterizing additional 3Dconformational information such as super-secondary, tertiary, quaternarystructures, twist and layers of sheets and helices, as well as thecontacts present between sheets and helices. Additionally, the systemcan analyze contacts or formation of protein complexes. These methodscan be combined with amide II (amide I-II) or with hydrogen/deuteriumexchange to separate different hydration states of the protein (solventexposed vs. buried) and identify conformational flexibility of differentsecondary structures.

Additional preferred embodiments provide for analysis of structuralchanges in time-dependent properties of proteins and other materials.Methods include applying to structural or kinetic information in anyother protein vibrational lineshapes, or cross-peaks between differentvibrational lineshapes. For example, ligand binding, proteinaggregation, association/dissociation, and catalysis.

Preferred embodiments further comprise quantifying transient-structureof proteins with non-equilibrium 2DIR. These methods can be applied toanalyze protein folding and function, including temperature-jump 2DIR,pH-jump, phototriggered ion release, and 2DIR spectroelectrochemistry(oxidation states).

These methods apply to different samples including membrane proteins,fibers, gels, and insoluble proteins. These can also be used foranalysis of DNA and RNA, such as Watson-Crick base pairing: A-DNA,B-DNA, Z-DNA, G-quartets, RNA Structure and fluctuations, and RNA Ligandbinding/Ion binding.

These methods provide for separating (bio) chemical mixtures bymonitoring cross peaks by structural Analysis (various isomers),chemical analysis, materials (Amorphous materials in particular) and forseparating and quantifying analytes in solution.

Characterizing heterogeneous samples with 2D lineshapes, such as, forexample: polydisperse polymer samples, materials such as mixtures,blends, and emulsions in which the same molecule may experiencedifferent environments within spatially distinct regions of the sample(down to angstrom/nanometer scale).

The invention can be used to distinguish the environment of a molecule,for example, determining the contacts with other molecules does ananalyte make. Heterogeneous samples can also be analyzed using preferredembodiments of the invention.

Analyzing bio-polymers can also be conducted in conjunction withthermodynamics, phase-transitions, conformational-transitions (forexample: helix-coil transitions).

Preferred embodiments provide different methods for generating librariesin analysis such as the use a set of standards to separate overlappingcontributions, identifying distinct cross peaks for a particularcompound, identifying distinct cross peaks for contacts between twomolecules, and the use lineshapes to distinguish the environment of amolecule.

FIG. 7 illustrates a process sequence for determining quantitativecharacteristics of an analyte. The system shown in FIGS. 1A and 1B canbe used to acquire spectral data that is analyzed 700 using a dataprocessor or computer system to provide quantitative and structuralcharacteristics of an analyte. After the acquisition 702 of 2D infrareddata, the processing system performs a transformation or deconvolution704 of the data to separate spectral features for the analyte. Thespectral features can then be further processed 706 to providequantitative characteristics of the analyte. This quantitativeprocessing 708 can include methods for estimating or predicting certaincharacteristics using, for example, linear decomposition 710 of thespectral data by principal component analysis or other analyticaltechniques, or alternatively by using multivariate linear or non-linearmethods 708, such as lease squares regression.

By separating 714 the individual components of the analyte using thespectral data and analytical processes, the concentration 716 of thecomponents in the analyte can be determined. Additionally, the structureof these components can be determined 718 such as the secondary,tertiary or higher order structure. The interactions 720 between thecomponents can also be determined, such as protein-protein orprotein-DNA interactions or ligand binding can also be determined.Certain non-equilibrium measurement conditions 722 can also be used totemporally alter the analyte and thereby measure and determine 724chemical kinetics and structural dynamics of the analyte. The resultingdata can be stored in memory 728 and displayed on a display 740.

Additionally, in certain preferred embodiments a stored library ofchemical compounds can be used to identify 730 spectral features andprovide for calibration. Also, the analytical results 714 can be used toadjust the system model or process 734 and provide for the formation ofsimulated data 732 based on the identified structures, frequencies andcoupling characteristics of a particular analyte.

Coherent two-dimensional infrared spectroscopy measures the correlationbetween vibrational frequencies. A cross peak is the result of excitinga given frequency (ω₁) and stimulating emission from a differentfrequency (ω₂). Cross peaks are only observed if two vibrations areconnected by electrostatic interactions, or if vibrational energytransfer occurs following excitation. Since vibrational energy transferonly occurs over short ranges, a cross peak is observed only if twovibrations share common atoms or reside within the same chemicalspecies. In solution, if two vibrations are due to two different species(molecules), no cross peaks are observed.

The term two-dimensional spectroscopy is also applied to a different(non-coherent) technique in which correlations between the intensitiesof peaks in a series of one-dimensional spectra are plotted on a secondfrequency axis. Spectra are collected as a function of an externalvariable such as temperature, concentration, or time. Peaks whoseamplitudes have the same dependence on the external variable can have ahigh correlation factor, whereas peaks that are independent are notcorrelated, and thus do not exhibit cross peaks. Note that this methoddoes not provide information on whether two vibrations arise from thesame molecule, or from different species, as long as the concentrationof the two species are correlated.

A preferred embodiment of the invention uses a coherent two-dimensionalversion of correlation spectroscopy: 2D/2D correlation spectroscopy.Instead of one-dimensional spectra, 2D infrared spectra are obtained andthe correlation coefficient between a pair of [ω₁ ω₃] frequencies andall other [ω₁ ω₃] frequencies within the series of spectra can berepresented, processed and recorded. The set of spectra is derived fromthe set of sixteen proteins used in the previous structural analysis(see FIG. 8). Note that since the correlation coefficient is independentof the order of [x,y] pairs, it is not necessary to sort the proteinspectra in order to calculate the correlation coefficients. In order toplot the correlation coefficients between all [ω₁ ω₃] pairs in our setof spectra, a four-dimensional plot is needed. Instead, by fixing thefirst set of frequencies and plotting the correlation of this set withother frequency pairs in the series.

FIG. 8 shows the correlation coefficient between [ω₁ ω₃]=[1684 1634]cm⁻¹ (labeled I) and all other pairs of frequencies in the spectrum. Theamplitude of the correlation coefficient is represented on the coloraxis. Solid contours correspond to a single spectrum of a mostlybeta-sheet protein, immunoglobulin-G overlaid for reference. The whitesquare represents the frequencies [1684 1634] cm⁻¹ used as the referencepoint for the correlation coefficients. This set of frequencies ischosen because it corresponds to a cross peak between the two maintransitions in beta sheets. FIG. 8 shows that the [1684 1634] cm⁻¹ iscorrelated with its mirror peak above the diagonal near [1634 1684] cm⁻¹(FIG. 8, labeled II) this is due to the fact that the two cross peaksare related to the two transitions in beta-sheet, so for proteins thathave a higher beta-sheet content, the two peaks will carry moreamplitude than for alpha-helical proteins, and thus the amplitude ofthese two peaks is correlated across the set. A similar interpretationcan be made for the correlation between the cross peak and the twodiagonal peaks (labeled III and IV). Also, note that each cross peak inthe 2D/2D correlation spectrum appears as a positive/negative doubletalong the detection axis. This is the result of vibrationalanharmonicity and it mirrors the spectrum as a whole. Also note thatdespite the broad and featureless peaks observed in the spectra the2D/2D correlation peaks, particularly the cross peaks are isolated andwell resolved.

In general, 2D/2D correlation spectroscopy is an intuitive method forisolating diagonals and cross peaks arising from particular structures(or analytes) in mixtures, even when the spectra are broad or highlycongested. More importantly, the described analysis method can be usedin order to assign spectral features of individual components in achemical mixture.

Additional examples of structural information contained in 2D infraredspectra are illustrated in connection with FIG. 9A in which aptamers areused to bind to specific molecules which can then be measured to obtain2D infrared spectra. Cross peaks (i.e. off diagonal spectral components)can be used to identify and separate conformational components using thesystems and methods described herein.

FIG. 9A shows the 2D spectrum of a short single-stranded DNA polymer,thrombin binding aptamer (TBA), in its unfolded form. As shown, thediagonal peaks can be assigned to specific vibrations of thedeoxy-guanosine (peaks A,B,B′ and D′) and deoxy-thymine (peaks D, C andE) bases that compose TBA, and the cross peaks indicate report on thethree-dimensional structure of the DNA polymer. The two-dimensionalspectrum can be used to measure the conformation of the DNA in solutionas well as the conformational changes associated with protein binding.

FIG. 9B illustrates an example in which hydrogen/deuterium exchange 2Dinfrared spectroscopy on Amide-I and Amide-II/II′ vibrations is used toassess the conformational flexibility and solvent exposure of secondarystructures in ubiquitin. Amide-II vibrations centered near 1550 cm⁻¹undergo a 100 cm⁻¹ frequency shift upon deuteration. In this example,protonated ubiquitin is dissolved in D₂O. Residues that are exposed tothe solvent undergo rapid H/D exchange whereas residues that are buriedwithin the core of the protein remain protonated. The cross peaksbetween Amide-II with Amide-I vibrations indicate the secondarystructure of those residues that have not undergone exchange, andsimilarly the cross peaks between Amide-II′ and Amide-I containinformation on the structure of the residues which are more exposed tothe solvent. In the current example it is observed that residues in thealpha-helix portion of ubiquitin undergo slow exchange whereas thebeta-sheet exchanges more rapidly. Spectra measured as a function oftime following reconstitution of the protein in D₂O, give directinformation on the kinetics of H/D exchange, such as the rate ofexchange (reaction) which can be directly related to the structuraldynamics of the secondary structures within the protein.

FIGS. 10A-10E illustrate the use of isotope labeling to identify andquantify distribution of conformers in an analyte. Isotope labels serveto de-couple and localize a single residue from the delocalizedexcitonic vibrations of the peptide backbone, providing an isolatedprobe of the structure and dynamics. The example depicted shows thestructures of the main conformers of the tryptophan zipper 2 peptide(TrpZip2). The peaks labeled (a,b) in the spectrum (FIG. 10A) arise froma single ¹³C═¹⁸O Cisotope lablel K8, and are assigned to two differentconformations of the beta-turn as shown in FIGS. 10B and 10C. In thepresent example, the two conformations exchange on the sub-millisecondtimescale, and since most analytical techniques cannot measureconformational changes on this short timescale, an “averaged-out”conformation is measured instead. This particular example highlights theenhanced structural content and fine time resolution available with 2Dinfrared spectroscopy. Similarly, different isotope labels enableseparation and the determination of the structure and concentration ofspecific conformers as shown in FIG. 10E.

Illustrated in FIGS. 11A-11C are spectra corresponding to threedifferent structures of the thrombin binding aptamer (TBA): chairconformation (folded), denatured (unfolded), and aggregate. The diagonaland off-diagonal peaks are very distinct for all three conformations.Similar to the example depicted in FIG. 9A, this example shows how thediagonal and off-diagonal peaks can be used to measure the molecularstructure of DNA and quantify the populations of the differentconformers in solution. In addition to molecular structure, 2D infraredmeasurements can characterize supra-molecular structure, such as thelong-range order present in TBA aggregates.

FIG. 12 illustrates generally the use of 2D infrared spectral analysisto detect and characterize different isomers contained in one or moreanalytes. The basic structural differentiation of stereoisomers, theseparation of different conformers and geometries and different opticalcharacteristics enantiomers/diastereomers can also be determined.Different bond conformations also have different vibrational spectraeven where mass and/or chemical composition is identical.

Illustrated in FIG. 13 is the application of 2D infrared to thequantitation of compounds commonly found in blood samples: creatinine,albumin, and urea. The three molecules have multiple overlapping peaksin the 1450-1750 cm⁻¹ region of the spectrum, and thus produce a verycongested absorption spectrum (diagonal), however the cross peaks, whichappear only when two vibrations correspond to the same species, arebetter isolated. These cross peaks can be integrated to extract theconcentrations of individual analytes in blood-samples or similarchemical mixtures.

Shown in FIG. 14 is a method of resolving isomeric mixtures, in thisexample the spectrum of a 50:50 mixture of two structural isomers ofnitrophenol (2-nitrophenol and 4-nitrophenol) is depicted as anillustration of how cross peaks in the spectrum can be used to perform a“spectroscopic separation” to identify and quantify each component inthe mixture. Similar chemical properties make structural isomersparticularly difficult to separate with conventional analytical methods.Most importantly, 2D infrared spectroscopy can be used to identifyisomers which are rapidly interconverting in solution. Conformationalisomers in particular, which are only differentiated by a rotationaround a single bond, can interconvert in the picosecond to millisecondtimescale. These isomers are difficult to separate using conventionaltechniques (such as chromatography), since a single isomer caninterconvert back to an equilibrium mixture in a few milliseconds.

Shown in FIGS. 15A and 15B is an illustration of the methods of usingcross peaks to separate components of mixtures in this example, amixture of organic compounds, such as acetone and hexane.

While the present invention has been described here in conjunction witha preferred embodiment, a person with ordinary skill in the art, afterreading the foregoing specification, can effect changes, substitutionsof equivalents and other types of alterations to the system and methodthat are set forth herein. Each embodiment described above can also haveincluded or incorporated therewith such variations as disclosed inregard to any or all of the other embodiments. Thus, it is intended thatprotection granted by Letters Patent hereon be limited in breadth onlyby definitions contained in the appended claims and any equivalentsthereof.

What is claimed is:
 1. A method for measuring a conformational structureof an analyte comprising: processing two-dimensional spectral data of ananalyte with a data processor to determine a plurality of conformationalcomponents of the analyte in a sample, the analyte comprising at leastone protein having a plurality of residues, and wherein the spectraldata includes amplitude and phase of a signal emitted by the analyte inresponse to a plurality of incident light pulses; determining, with thedata processor, a first quantitative value of a first conformationalcomponent of the analyte, the first quantitative value including a firstfraction of a first residue for the first conformational component; anddetermining, with the data processor, a second quantitative value of asecond conformational component of the analyte, the second quantitativevalue including a second fraction of the first residue for the secondconformational component.
 2. The method of claim 1 further comprisingdetermining a concentration of the first conformational component. 3.The method of claim 1 further comprising detecting two dimensionalinfrared spectral data of a sample containing the analyte.
 4. The methodof claim 3 wherein the step of detecting spectral data comprisesilluminating the sample with a plurality of infrared wavelengths anddetecting a plurality of wavelengths with a detector.
 5. The method ofclaim 1 further comprising processing the spectral data with a dataprocessor.
 6. The method of claim 1 wherein the step of determining thefirst quantitative value provides a relative percentage of the firstconformational component.
 7. The method of claim 1 wherein the analytecomprises a protein.
 8. The method of claim 1 further comprising using alabel to identify a conformational structure of the analyte.
 9. Themethod of claim 1 wherein the processing step comprises separatingcomponents of the analyte using spectral cross peaks.
 10. The method ofclaim 1 further comprising performing a linear decomposition of thespectral data.
 11. The method of claim 1 further comprising determiningsecondary, tertiary or higher order components of the analyte.
 12. Themethod of claim 1 further comprising detecting time varying infraredspectral data.
 13. The method of claim 7 further comprising determininga concentration of an a-helix conformation.
 14. The method of claim 7further comprising determining a concentration of a β-sheetconformation.
 15. The method of claim 1 further using electronicallystored data to determine a conformer in the analyte.
 16. The method ofclaim 1 further comprising displaying two dimensional vibrationalspectra on a display.
 17. The method of claim 1 further comprisingdetermining a size of a component in the sample.
 18. The method of claim1 further comprising determining a correlation coefficient of thesample.
 19. The method of claim 1 further comprising illuminating thesample with at least four non-colinear beams of light generated with afirst laser source and a second laser source pulse that is temporarilysynchronized with the first laser source.
 20. The method of claim 1,wherein the two dimensional spectral data includes a two dimensionalplot of excitation frequency versus detection frequency.
 21. The methodof claim 20, further comprising identifying common residues based oncross peaks in the two dimensional plot.
 22. The method of claim 1,wherein the two dimensional spectral data is generated using at least apair of infrared pulses optically coupled to the analyte and a furtherpulse to stimulate an emitted signal.
 23. The method of claim 22,wherein the emitted signal is detected in the frequency domain using agrating spectometer.
 24. The method of claim 1, wherein the twodimensional spectral data is generated using a pair of excitation pulsesdelayed in time and measuring a signal spectrum as a function of thetime delay between the excitation pulses.
 25. The method of claim 24,wherein the processing of the two dimensional spectral data furthercomprises performing a Fourier transform along the excitation time delayto obtain excitation frequency.
 26. The method claim 1, wherein the twodimensional spectral data is derived by measuring both amplitude andphase of an emitted electric field from the sample.
 27. The method ofclaim 26, wherein the measuring the amplitude and phase of the emittedelectric field includes utilizing an external reference beam orutilizing an internal reference pulse.
 28. The method of claim 1,wherein the two dimensional spectral data is derived utilizing a pair ofnon-colinearly focused excitation pulses and overlapping an emittedsignal with an external reference pulse.
 29. The method of claim 1,wherein the two dimensional spectral data includes excitation frequencyversus detection frequency, excitation time versus detection frequency,excitation frequency versus detection time, or excitation time versusdetection time.
 30. The method of claim 1, further comprisingdetermining a ratio of a first isomer to a second isomer in the analyte.31. The method of claim 1 further comprising adjusting a temperature ofthe sample.
 32. The method of claim 31 further comprising using a T jumplaser to adjust the temperature of the sample to measure spectral dataat different temperatures.
 33. A system for measuring vibrationalspectral data of an analyte comprising: a light source system thatprovides illuminating light pulses at a plurality of wavelengths; anoptical system that delivers the illuminating light pulses onto ananalyte; a detector system that detects light from the analyte toprovide spectral data including amplitude and phase of the detectedlight from the analyte, the analyte comprising at least one proteinhaving a plurality of residues including a first residue; and a dataprocessing system that processes two dimensional amplitude and phasespectral data of the analyte to determine a first fraction of the firstresidue of a first conformational component of the analyte and a secondfraction of the first residue of a second conformational component ofthe analyte.
 34. The system of claim 33 further comprising a computerprogram that determines a concentration of a conformational component.35. The system of claim 33 further comprising a two dimensional detectorarray that detects two dimensional infrared spectral data of a samplecontaining the analyte.
 36. The system of claim 33 further comprising acomputer program that determines the quantitative characteristic anddetermines a relative percentage of the conformational component. 37.The system of claim 33 wherein the analyte comprises a protein.
 38. Thesystem of claim 33 further comprising a label to identify aconformational structure of the analyte.
 39. The system of claim 33wherein the system detects spectral data wherein the sample isilluminated with a plurality of infrared wavelengths and the detectionsystem detect a plurality of wavelengths with a detection.
 40. Thesystem of claim 33 wherein the processing separates components of theanalyte using spectral cross peaks.
 41. The system of claim 33 whereinthe processing system performs a linear decomposition of the spectraldata.
 42. The system of claim 33, wherein the two dimensional spectraldata comprises an excitation frequency dimension and a detectionfrequency dimension.
 43. The system of claim 42, wherein the dataprocessor identifies common residues based on cross peaks in the twodimensional data.
 44. The system of claim 33, wherein the light sourcesystem generates a pair of infrared pulses to illuminate the analyte anda third pulse to illuminate an emitted signal.
 45. The system of claim44, wherein the emitted signal is detected in the frequency domain usinga grating spectometer.
 46. The system of claim 33, wherein the twodimensional spectral data is derived using a pair of excitation pulsesdelayed in time and measuring a signal spectrum as a function of theexcitation time delay between the excitation pulses.
 47. The system ofclaim 46, wherein the derived two dimensional spectral data furthercomprises Fourier transformed spectral data along the excitation timedelay to obtain excitation frequency.
 48. The system claim 33, whereinthe two dimensional spectral data is generated by detecting amplitudeand phase of an electric field received from the analyte.
 49. The systemof claim 48, wherein the detector system detects amplitude and phase ofthe electric field by utilizing an external reference beam or utilizingan internal reference pulse.
 50. The system of claim 33, wherein the twodimensional spectral data is derived utilizing a pair of non-collinearlyfocused excitation pulses and overlapping an emitted signal with anexternal reference pulse.
 51. The system of claim 33, wherein the twodimensional spectral data includes excitation frequency versus detectionfrequency, excitation time versus detection frequency, excitationfrequency versus detection time, or excitation time versus detectiontime.
 52. The method of claim 33, wherein the light source systemgenerates a plurality of temporally displaced light pulses.
 53. Thesystem of claim 52, wherein a first pulse is colinear with a secondpulse.
 54. The system of claim 52 wherein a first pulse is non colinearwith a second pulse.
 55. The system of claim 52, wherein the lightsource system generates at least a first pulse, a second pulse and athird pulse.
 56. The system of claim 33, further comprising a pluralityof non colinear beams including a beam split to form a local oscillatorand a tracer.
 57. The system of claim 33 further comprising atemperature controller to adjust the temperature of the analyte todetect spectral data at a plurality of different temperatures.
 58. Amethod for measuring a conformational change of an analyte componentcomprising: detecting two dimensional spectral data of an analyte todetermine a characteristic of a conformational component of the analyte,the spectral data including amplitude and phase of detected light fromthe analyte; processing the spectral data with a data processor todetermine a first quantitative value of a conformational component ofthe analyte; and subsequently, after a change in a concentration in theconformational component, determining a second quantitative value of theconformational component of the analyte.
 59. The method of claim 58further comprising determining a concentration of the firstconformational component.
 60. The method of claim 58, further comprisingdetecting two dimensional infrared spectral data of a sample containingthe analyte.
 61. The method of claim 60 wherein the step of detectingspectral data comprises illuminating the sample with a plurality ofinfrared wavelengths and detecting a plurality of wavelengths with adetector.
 62. The method of claim 58, further comprising processing thespectral data including a first polarization component and a secondpolarization component with the data processor.
 63. The method of claim58 wherein the step of determining the first quantitative value providesa relative percentage of the first conformational component.
 64. Themethod of claim 58 wherein the analyte comprises a protein having aplurality of residues, the method further comprising determining afraction of one of the plurality of residues to determine the firstquantitative value and subsequently determining a second fraction ofsaid one of the plurality of residues after the change in concentration.65. The method of claim 64 further comprising determining aconcentration of an a-helix conformation.
 66. The method of claim 64further comprising determining a concentration of a β-sheetconformation.
 67. The method of claim 58 further comprising using alabel to identify a conformational structure of the analyte.
 68. Themethod of claim 58 wherein the processing step comprises separatingcomponents of the analyte using spectral cross peaks.
 69. The method ofclaim 58 further comprising performing a linear decomposition of thespectral data.
 70. The method of claim 58 further comprising determiningsecondary, tertiary or higher order components of the analyte.
 71. Themethod of claim 58 further comprising detecting time varying spectraldata.
 72. The method of claim 58 further using electronically storeddata to determine a conformational component in the analyte.
 73. Themethod of claim 58 further comprising displaying two dimensionalvibrational spectra on a display.
 74. The method of claim 58 furthercomprising determining a size of a component in a sample.
 75. The methodof claim 58 further comprising determining a correlation coefficient ofa sample.
 76. The method of claim 58 further comprising illuminating thesample with at least four non-colinear beams of light and a second lasersource pulse.
 77. The method of claim 58 further comprising processingspectral data of a blood analyte.
 78. The method of claim 58 furthercomprising performing a nonlinear decomposition of the spectral data.79. The method of claim 58 further comprising controlling delays in anoptical system that couples a light source system to a sample containingthe analyte.
 80. The method of claim 79 wherein the controlling stepcomprises adjusting one or more notarized stages.
 81. The method ofclaim 58 further comprising controlling a polarization of one or morebeam paths coupled to a sample containing the analyte.
 82. The method ofclaim 58 further comprising detecting a reflected third order signal anda local oscillator beam on a stripe of a detector and detecting a secondpair of pulses with a further stripe of the detector.
 83. The method ofclaim 58 further comprising processing data using singular valuedecomposition.
 84. The method of claim 58 further comprising adjusting atemperature of the analyte to obtain spectral data at differenttemperatures.
 85. The method of claim 58 further comprising using alaser to control a temperature of the analyte.